What Was Once Learned May Need to Be Unlearned: Machine Unlearning for Deprecated API Knowledge in Large Language Models

2026-09-22

AI Summary

研究针对大型语言模型生成废弃API的问题，通过构建MUDAPIBench基准测试并评估多种机器遗忘方法，发现梯度差异法在抑制废弃API的同时保持其他能力方面表现最佳。

Abstract

Large language models (LLMs) for code completion may generate deprecated APIs because their pre-training corpora contain code from historical library versions. Existing approaches use inference-time intervention, model editing, or machine unlearning, but multiple plausible completions make predefined replacements restrictive. Moreover, existing studies rarely verify whether models exhibit the targeted deprecated behavior or evaluate unintended changes to other APIs. We conduct a systematic empirical study of machine unlearning for deprecated API knowledge and construct MUDAPIBench, a behavior-grounded benchmark with over 7,000 model-specific instances derived from 145 deprecated-to-up-to-date API mappings across eight Python libraries. Instances are retained only when the original model generates the target deprecated API. We evaluate eight representative unlearning methods across three code LLMs on deprecated API forgetting, up-to-date API generation, preservation of other and unrelated API behaviors, general code-generation capability, and efficiency. Results show that Gradient Difference (GD) provides the best overall trade-off, suppressing deprecated APIs while preserving other capabilities with moderate computational costs. Further analyses reveal substantial variation across libraries and show that APIs deprecated after the model's training-data cutoff are harder to forget. Layer-wise analyses indicate that GD achieves effective forgetting with comparatively controlled internal changes.

Problem

• machine unlearning
• deprecated API
• large language models
• code completion

Innovation

• machine unlearning
• MUDAPIBench
• Gradient Difference
• deprecated API